CIRSS Seminar - Library Cultures of Data Curation: Adventures in Astronomy
Friday, March 1, 2019
4pm - 5pm
126 IS
4pm - 5pm
126 IS
Event Details
Session leaders: Peter Darch, iSchool Assistant Professor
Description: Research datasets often remain valuable long after their initial production. They may be reused to produce new research findings or to reproduce published results to verify their integrity. However, few scientific projects are funded or managed in ways that ensure data preservation beyond fiscal close. To address the challenge of long-term research data curation, many university libraries are implementing services centered on provision of digital infrastructure for data. This talk presents a study, comprising interviews (n=39) and ethnographic observation, of two university libraries who partnered with the Sloan Digital Sky Survey (SDSS) collaboration to curate a significant astronomy dataset. SDSS was a major astronomy project that made data available to researchers on an unprecedented scale. The data transfer from SDSS to the two libraries was the largest attempted at the time. The paths taken by the two libraries in terms of their approach to curation diverged considerably. Each libraries’ activities afforded different possibilities for dataset reuse. These differences were driven by factors specific to each library, including their strategic objectives for undertaking the transfer and existing infrastructure at their disposal (including technical infrastructure, staff expertise, values and internal culture, and organizational structure). Because these factors vary across libraries and are deep-rooted, library digital curation services for datasets are unlikely to converge as they mature. However, the discrepancies between library services and initial data producer expectations afford remarkable prospects for enabling data use and reuse by and for purposes not known at the outset of data collection.
* Biosketch:
Peter Darch is an assistant professor in the School of Information Sciences at the University of Illinois at Urbana-Champaign. Prior to joining the iSchool at Illinois, Darch worked as a postdoctoral researcher in the UCLA Department of Information Studies and Center for Knowledge Infrastructures, with which he continues to collaborate on studies of the building, running, and effects of information infrastructures that support scientific collaboration. His dissertation for the DPhil in computer science from the University of Oxford (2012) addressed how scientists and software engineers in online citizen science projects manage members of the public to process and generate large datasets. He is particularly interested in the changes in the organization and conduct of contemporary scientific research that result from the interaction of technologies that afford collection of increasing quantities and types of scientific data with broader social and cultural factors. To study these changes, he conducts long-term ethnographic studies of large, multidisciplinary scientific projects to examine relationships between contexts in which these collaborations are embedded, information infrastructures, and scientists’ day-to-day data practices.
Description: Research datasets often remain valuable long after their initial production. They may be reused to produce new research findings or to reproduce published results to verify their integrity. However, few scientific projects are funded or managed in ways that ensure data preservation beyond fiscal close. To address the challenge of long-term research data curation, many university libraries are implementing services centered on provision of digital infrastructure for data. This talk presents a study, comprising interviews (n=39) and ethnographic observation, of two university libraries who partnered with the Sloan Digital Sky Survey (SDSS) collaboration to curate a significant astronomy dataset. SDSS was a major astronomy project that made data available to researchers on an unprecedented scale. The data transfer from SDSS to the two libraries was the largest attempted at the time. The paths taken by the two libraries in terms of their approach to curation diverged considerably. Each libraries’ activities afforded different possibilities for dataset reuse. These differences were driven by factors specific to each library, including their strategic objectives for undertaking the transfer and existing infrastructure at their disposal (including technical infrastructure, staff expertise, values and internal culture, and organizational structure). Because these factors vary across libraries and are deep-rooted, library digital curation services for datasets are unlikely to converge as they mature. However, the discrepancies between library services and initial data producer expectations afford remarkable prospects for enabling data use and reuse by and for purposes not known at the outset of data collection.
* Biosketch:
Peter Darch is an assistant professor in the School of Information Sciences at the University of Illinois at Urbana-Champaign. Prior to joining the iSchool at Illinois, Darch worked as a postdoctoral researcher in the UCLA Department of Information Studies and Center for Knowledge Infrastructures, with which he continues to collaborate on studies of the building, running, and effects of information infrastructures that support scientific collaboration. His dissertation for the DPhil in computer science from the University of Oxford (2012) addressed how scientists and software engineers in online citizen science projects manage members of the public to process and generate large datasets. He is particularly interested in the changes in the organization and conduct of contemporary scientific research that result from the interaction of technologies that afford collection of increasing quantities and types of scientific data with broader social and cultural factors. To study these changes, he conducts long-term ethnographic studies of large, multidisciplinary scientific projects to examine relationships between contexts in which these collaborations are embedded, information infrastructures, and scientists’ day-to-day data practices.
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